Can Yi Coder 9B Chat run on RX 6700 XT 12GB?

YES — Runs Great

C54Usable

Estimated from fit model

Yi Coder 9B Chat needs ~8.6 GB VRAM. RX 6700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.6 GB, 36.4 tok/s, Runs well

8.6 GB required12.0 GB available

72% VRAM used

Fit status

Runs well

Decode

36.4 tok/s

TTFT

5323 ms

Safe context

67K

Memory

8.6 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on RX 6700 XT 12GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 36.4 tok/s decode · 5.3s TTFT (warm) · 91 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	36.4 tok/s	2903 ms	67K
Coding	C	Runs well	36.4 tok/s	5323 ms	67K
Agentic Coding	C	Runs well	36.4 tok/s	7742 ms	67K
Reasoning	C	Runs well	36.4 tok/s	6291 ms	67K
RAG	C	Runs well	36.4 tok/s	9678 ms	67K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on RX 6700 XT 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C52
Q4_K_M	4	5.5 GB	Medium	C53
Q5_K_M	5	6.5 GB	High	C52
Q6_KBest for your GPU	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Yi Coder 9B Chat on your machine.

Run

lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Yi Coder 9B Chat

RTX 5070 Ti 16GBOpción económica

16 GB VRAM (+4)896 GB/s (+512)

Sube la velocidad estimada de decodificación alrededor de un 187%.104.5 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 187%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$749 MSRP

RTX 4070 Ti Super 16GBMejor relación calidad-precio

16 GB VRAM (+4)672 GB/s (+288)

Sube la velocidad estimada de decodificación alrededor de un 148%.90.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 148%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$799 MSRP

Frequently asked questions

Can RX 6700 XT 12GB run Yi Coder 9B Chat?

Yes, RX 6700 XT 12GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 36.4 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B Chat?

The recommended quantization for Yi Coder 9B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 9B Chat run at on RX 6700 XT 12GB?

On RX 6700 XT 12GB, Yi Coder 9B Chat achieves approximately 36.4 tokens per second decode speed with a time-to-first-token of 5323ms using Q4_K_M quantization.

Can RX 6700 XT 12GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on RX 6700 XT 12GB receives a C grade with 36.4 tok/s and 67K context.

What context window can Yi Coder 9B Chat use on RX 6700 XT 12GB?

On RX 6700 XT 12GB, Yi Coder 9B Chat can safely use up to 67K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 6700 XT 12GB See all hardware for Yi Coder 9B Chat

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